I'm dealing with a case in which the nodes are not connected enough to let vis place them correctly without overlapping.
As mentioned in the title the network consists of 8 undertress.
I attached two pictures for better understanding.
with physics off
with physics on
I read about clusters but don't know if this feature would solve the problem.
Thank you in advance
I found a working solution for me:
I connected the nodes of the subtrees with the most relations (incoming and outgoing edges) with each other.
Then I turned physics on and turned it of after the scene has "settled".
network.on("stabilizationIterationsDone", function () {
network.setOptions( { physics: false } );
console.log(network.getSeed());
});
I refreshed the scene a few times and took the desired seed as randomSeed in the layout option.
I got the hint of using physics from Denis M.s blogpost:
link to the blogpost
Related
I am new in ableton live software. I would like to understand why for some instruments I can play several notes at the same time (and create chord progression) and for the others I can hear only one note of a chord.
For example, there are two guitars: 'Power Chords Guitar' and 'Please Rise For Jimi Guitar'. Both of them are basing on an operator. For the first one I am able to press several buttons on midi keyboard and hear a sound, for the second I can hear only one note of a chord.
I was trying to compare operator options, but I was unable to find the setting which causes this multinote/mononote functionality.
Thank you very much for your help.
J
Ableton's Operator consists of 4 monophonic oscillators. Depending on a preset, it can be setup to play polyphony. It seems that the Glide function is responsible for that effect. Make sure you have at least 2 oscillators (the more the more poly) with Glide "on" set.
Check out Ableton's webpage for reference on how to use its instruments.
I've set up a simple test case to finally learn some graph databases
I have a simple tree structure based on a collection of roughly 80000 vertices/documents, with around 25 attributes each. The only edges are outbound "is_parent" edges, so to find children of each node, I can simple pick up all inbound edges. I have not set up any specific indices on any fields.
The tree is 20 levels deep and I'm grabbing a random node on the fifth level, to then pick up all descendants of that node using a graph traversal:
FOR t IN GRAPH_TRAVERSAL("sample_tree", "sampleunit/2565130142666", "inbound", {"maxDepth":20}) RETURN t'
This takes a little more than 3 seconds on my dev machine and I feel I might be doing something wrong. Is there any way to speed things up or do I have any conceptual issues?
I set up an example tree-like graph as you described and ran the query on it.
Interestingly, the following query was executing much faster than your query:
FOR t IN TRAVERSAL(sampleunit, unitlinks, "sampleunit/2565130142666", "inbound", {"maxDepth":20}) RETURN t
The query above uses the "older" traversal function in AQL. We checked why there is a performance difference between the two traversal types, and finally found something that can be improved.
The fix for this has been pushed into the 2.2 and devel branches. It is included in commit 9a1eb149aa4da514d709c43a4ebdfd8819ba2f1d if you prefer cherry-picking.
I see similar issue between NEIGHBORS and GRAPH_NEIGHBORS on version 2.6.3, the first is 30 times faster then the second.
My question in short - is whether I can modify the traversal logic used by Neo4j - how to have a control over which edges are traversed and which aren't, during the reachbility computation.
Full description:
I am considering migrating from our current DB to neo4j, and I am wondering if neo4j is a good fit for the following task:
We have large graphs with about 10M simple nodes - their attribute is only a single id.
We also have 3 kinds of edges - "standard", "opening" and "closing". The "opening" and "closing" ones also have a "color" attribute, so they are matched. Each "opening" edge has exactly one matching "closing" edge". For example, there is one opening edge colored as "3", so there's also one closing edge colored the same.
We need to solve a reachability between two nodes where the rules of traversal are fairly simple:
You can go through standard edges as you want, you can go through opening edges as you want, while maintaining the order of visited "opening" edges in a stack BUT (that's the tricky part) when you come to a junction that has several "closing" edges, you MUST go through the closing edge which matches the last "open" edge encountered, and then pop out that "open" edge from the stack.
For example:
a -[STANDARD]->B-[Open color:3]->C-[Standard]->D-[Close color:3]->E
and also
D-[Close color:4]->F
note that D has two "closing" edges, with different colors.
By the rules defined above, E is reachable by A as the color stack has [3] on its top.
F, however, is not reachable by A.
Can neo4j be configured for such graph traversal logic?
Thanks!!
This is possible by implementing your own PathExpander and passing to the TraversalDescription. As Michael Hunger pointed out: BranchState can be used to optimize your expander so that you don't have to check the full path each expansion, but instead some kind of boiled down (immutable mind you) state that each traversal branch carries. The expander can pass on a modified state to each next step.
Unfortunately the neo4j manual lacks good examples of using branch state. This sounds like an awesome usage though!
So I have an un-directed un-weighted graph. It contains cycles. I would like to find the path which visits the most nodes with no repeat visits to any node. Since this is a graph traversal, you can start and end at any node you like.
Background Research:
I have looked at Travelling Salesman Problem (TSP); this problem is different and does NOT allow you to finish where you started from and there are no weights. I have looked at several other algorithms, but have found none suitable for this problem.
Graph Size: There are 100 nodes in the graph; with 10 disconnected nodes.
UPDATE: I have moved this to: https://math.stackexchange.com/questions/243375/what-is-the-maximum-number-of-nodes-i-can-traverse-in-an-undirected-graph-visiti
Look for the Hamiltonian Cycle problem
http://en.wikipedia.org/wiki/Hamiltonian_cycle
You should take a look at the wikipedia entry which has an algorithm for acyclic graphs. Your graph has cycles which makes your problem NP-hard.
I would try and create a DAG with nodes representing strongly connected components. Then you could at least find the path that visits the most strongly connected components. You could then expand that path by replacing the individual (strongly connected components) nodes with the longest paths in each of the subgraphs.
Finding the longest paths in the subgraphs is now the same as your original problem but at least you graphs are smaller. If your in luck, the subproblems are easy and your done. In the general case they might not be so small and you could use some advanced heuristics. Maybe have a look at this paper or this question (you could use the answer there to solve your problem completely but i'm not sure)
I am currently trying to find the method or mechanism for calculating the distance between two wireless mobile nodes.
Let's say, there are 2 nodes (node B and C) coming in communication with node A. How can node A knows whether node B or node C is nearer than the other?
Is it possible to find it out by using RSSI? or are there any other mechanisms to calculate the distance?
I would appreciate any of your answers or comments.
Thank you in advance.
How about using GPS? Most mobile devices have internal GPS support these days.
The RSSI will give you some indication of distance, but signal may have bounced around and penetrated several walls, so a stronger signal doesn't necessarily mean they are closer.
Another option is to use WiFi geolocation, which works pretty good assuming there are several other WiFi access points in the area. I use SkyHook and it works great:
http://www.skyhookwireless.com/location-technology/sdk.php